Probabilistic Mixture of Hyperbolic Mamba for Few-Shot Class-Incremental Learning

Few-shot class-incremental learning (FSCIL) grapples with the dual challenge of learning new classes from minimal labeled training data while alleviating catastrophic forgetting of previous learned classes. Compared with previous methods employing static adaptation on specific parameters, current works verify that dynamic weights and sequence modeling in Selective State Space Models (SSMs) can capture distinctive feature drifts in FSCIL. However, the flattening operation in SSMs fragments the latent semantic relationship, where the resulting task isolation and representation degeneration are detrimental to FSCIL. Toward this issue, this paper presents a novel framework named Probabilistic Mixture of Hyperbolic State Space Experts (PmH-SSE) for FSCIL. First, since SSMs rely on scanning as an alternative to self-attention, the Hyperbolic state space model with multi-scale hybrid scan is built to facilitate few-shot learning by providing an extra Hyperbolic geometry that encodes hierarchical relationships. Moreover, we propose the probabilistic mixture of Mamba to increase the model's flexibility in handling non-stationary data streams in FSCIL and enhance the stability of high-parameter models in few-shot conditions. Finally, under the same experimental conditions, the proposed PmH-SSE demonstrates superior performance in comprehensive experiments. The codes are available at https://github.com/yawencui/PmH-SSE.